Index
Mariah S., Ryan L., Shane F., Sumeet W.
5/15/2020
Introduction
During the strange and uncertain times we are currently living in, our group thought it fitting to use data related to the COVID-19 outbreak and try to understand how it has impacted (directly or indirectly) people across the country. Due to the stay-at-home orders issued across the country, many people find themselves at home for much longer than usual. We are collecting data on Energy Usage, Mobility Trends, and Death Rates. We will unpack these data sets to analyze both the environmental impact that COVID-19 (and the following human behavior) had, and the excess deaths beyond those attributed to COVID-19. By studying these data sets, we hope to learn more about how the COVID-19 outbreak has impacted the country beyond just how many are sick or dead because of the virus.
Summary
For our summary information we focused the function on the Mobility Trends dataset to get a better understanding of movement during COVID-19. The Mobility Trends dataset contains 224004 rows that explains the volume of information that is currently available and 8 columns with the following column names:
sub_region_1,
date,
retail_and_recreation_percent_change_from_baseline,
grocery_and_pharmacy_percent_change_from_baseline,
parks_percent_change_from_baseline,
transit_stations_percent_change_from_baseline,
workplaces_percent_change_from_baseline,
residential_percent_change_from_baseline
These columns are critical in knowing where the regional information is arriving from, the day the data originates from, and what specific groupings the mobility percent changes are connected to. Additonally, to understand the data, our summary function displays two average national mobility percent changes, one in reference to an essential activity such as the grocery and pharmacy national average of 0.3574185. As well as non-essential activity such as the retail and recreation national average of -14.1604293. These two averages imply the importance of population mobility as a whole during a pandemic.
Summary Table
The table provides a condensed form of information on each category for the State. Grouping by state, we calculated the mean for the category over the date range to show the average change in pattern of behavior.
The largest decreases were in the categories of work, transit, and retail. This was juxtaposed by the increase in park activity.
| State | Workplace | Parks | Grocery | Retail | Transit | Residence |
|---|---|---|---|---|---|---|
| Alabama | -18.86 | 13.62 | 2.86 | -10.50 | -11.08 | 7.46 |
| Alaska | -20.41 | 25.88 | -4.01 | -12.84 | -28.41 | 8.23 |
| Arizona | -22.64 | -4.25 | -2.53 | -18.10 | -13.18 | 7.84 |
| Arkansas | -17.12 | 29.18 | 4.96 | -5.81 | -9.39 | 6.67 |
| California | -26.29 | -6.34 | -4.00 | -24.12 | -25.29 | 10.69 |
| Colorado | -26.78 | 0.68 | -5.14 | -19.99 | -21.81 | 10.18 |
| Connecticut | -27.87 | 22.38 | -7.17 | -23.03 | -29.48 | 11.92 |
| Delaware | -25.27 | 11.17 | -8.83 | -19.85 | -24.25 | 10.25 |
| District of Columbia | -40.18 | -32.39 | -18.02 | -39.06 | -47.28 | 15.52 |
| Florida | -21.95 | -15.05 | -3.91 | -18.01 | -22.31 | 8.92 |
| Georgia | -20.69 | 3.78 | 1.67 | -10.88 | -12.67 | 8.59 |
| Hawaii | -28.30 | -38.80 | -18.09 | -32.79 | -43.52 | 12.12 |
| Idaho | -20.94 | 44.08 | 4.24 | -10.46 | -7.34 | 6.40 |
| Illinois | -22.24 | 15.75 | 3.65 | -15.12 | -15.16 | 9.22 |
| Indiana | -22.91 | 25.62 | 2.18 | -13.50 | -9.36 | 8.92 |
| Iowa | -18.45 | 38.27 | 6.89 | -15.08 | -9.62 | 8.41 |
| Kansas | -20.01 | 51.39 | 0.35 | -11.25 | -8.03 | 8.35 |
| Kentucky | -22.23 | 21.62 | 3.58 | -8.99 | -10.80 | 8.89 |
| Louisiana | -21.15 | -4.75 | 3.18 | -11.19 | -17.58 | 8.65 |
| Maine | -23.43 | 10.69 | -3.15 | -17.88 | -23.75 | 8.75 |
| Maryland | -26.69 | 17.80 | -7.33 | -20.11 | -26.79 | 11.99 |
| Massachusetts | -31.64 | 21.90 | -9.42 | -25.67 | -35.06 | 13.13 |
| Michigan | -28.01 | 26.33 | -3.30 | -21.12 | -23.98 | 11.17 |
| Minnesota | -21.96 | 23.51 | -2.10 | -16.73 | -23.49 | 10.11 |
| Mississippi | -20.70 | 0.04 | 3.64 | -6.99 | -10.84 | 7.56 |
| Missouri | -16.96 | 23.27 | 6.84 | -7.32 | -7.94 | 7.53 |
| Montana | -22.22 | 16.93 | 0.06 | -16.92 | -15.62 | 7.73 |
| Nebraska | -18.01 | 43.53 | 2.27 | -12.17 | -0.95 | 8.29 |
| Nevada | -25.69 | -13.50 | 4.09 | -16.51 | -11.83 | 9.15 |
| New Hampshire | -25.17 | 15.45 | -4.63 | -18.97 | -25.41 | 10.93 |
| New Jersey | -31.81 | 14.26 | -7.81 | -30.50 | -30.88 | 14.01 |
| New Mexico | -23.25 | -2.60 | 3.94 | -13.84 | -11.33 | 8.24 |
| New York | -27.31 | 15.95 | -2.58 | -23.30 | -29.53 | 11.78 |
| North Carolina | -20.44 | 10.72 | 1.24 | -12.25 | -16.41 | 8.12 |
| North Dakota | -19.03 | -5.15 | -0.42 | -16.66 | -17.49 | 9.33 |
| Ohio | -23.26 | 39.80 | 0.26 | -15.16 | -9.75 | 9.61 |
| Oklahoma | -20.08 | 2.29 | 5.34 | -5.86 | -5.05 | 7.39 |
| Oregon | -23.17 | 24.66 | -1.34 | -15.67 | -14.16 | 7.58 |
| Pennsylvania | -25.05 | 22.27 | -5.04 | -19.91 | -23.07 | 10.37 |
| Rhode Island | -28.19 | 26.89 | -10.30 | -21.84 | -37.01 | 11.36 |
| South Carolina | -19.41 | 2.81 | 2.22 | -11.28 | -11.11 | 7.59 |
| South Dakota | -21.59 | 39.30 | 3.05 | -15.05 | -11.92 | 8.70 |
| Tennessee | -19.88 | 11.50 | 5.35 | -8.00 | -11.01 | 7.53 |
| Texas | -20.15 | -2.63 | -0.12 | -10.40 | -11.10 | 8.83 |
| Utah | -23.30 | 32.71 | 6.97 | -10.01 | -7.89 | 8.21 |
| Vermont | -29.50 | 7.01 | -8.57 | -21.18 | -36.05 | 10.46 |
| Virginia | -22.76 | 9.26 | -0.68 | -13.62 | -15.97 | 9.38 |
| Washington | -26.20 | 24.61 | -0.84 | -16.75 | -18.00 | 8.93 |
| West Virginia | -21.75 | -7.01 | -2.11 | -11.36 | -14.74 | 7.96 |
| Wisconsin | -21.06 | 24.96 | -2.80 | -19.39 | -14.93 | 9.82 |
| Wyoming | -20.25 | 3.46 | 4.37 | -14.88 | -4.01 | 7.73 |
Graph of Change in Social Movements During Quarantine
We chose to graph the change in society’s movements over time, relative to the baseline that has been recorded by Google. The lines colored lines representing categories show the change in habits over time and the efficacy of the stay home orders.
The lines indicate that the stay home orders have been effective due to the rise in the residential category. The only Thing that drastically increased was the category representing parks. The other categories show significant decrease. The grocery category shows little change signaling essential operation.
Graph of Death Rates During the Pandemic Year
We wanted to represent the change in mortality rates for the year during the pandemic. This graph represents the number of deaths above the normal baseline. New York seems to have had the most drastic impact for the year. The states with a drastically higher number of deaths can be compared states’ pandemic mobility trends shown in the next map.
Map of State Mobility Patterns
To get a better idea as to how much people in the US are still going out, the following map shows the states colored by the percentage change from baseline in their mobility habits overall (lighter blue represents a more mobile population). Hovering over a state reveals the state’s name and its mobility data by category. The states in the center of the country shows the least decrease in travel and the least efficacy in social distancing. The most drastic effects of quarantine seem to be in California and Florida.